It’s a problem even in the most bike-friendly cities: cars move faster than bikes, which means cyclists are put in danger when traffic lights change. To cut down on the danger of deadly crashes, a handful of California cities, including Pleasanton, Redding, and Monterey, are installing the Intersector, a smart traffic signal that can differentiate between bikes and cars–and then times traffic light changes accordingly.
The Intersector uses a microwave radar gun that can calculate the speed and length of approaching objects, so it knows whether a bike or car is rolling up. The device then decides how long the light should stay green so that both cars and bikes have enough time to pass through. Cars get four seconds to roll through, while bikes get 14 seconds. If a cyclist pedals through a light that’s already green when they arrive, the Intersector tacks on an extra five seconds of green. If no cars are coming, it can shorten the length of a green light to let people needlessly waiting at a red go ahead sooner.
Check out the Intersector in action below:
It’s not that Pleasanton, Redding, and Monterey have a soft spot for cyclists. In 2008, California’s AB 1581 law required counties and cities begin to replace service traffic signals with ones that can sense cyclists and motorcyclists, according to Government Technology. All new traffic signals must have this capability as well.
Pleasanton also uses video camera detection and inductive loops–metal detectors placed in the ground that alert traffic signals when cyclists are coming. Neither of these techniques are as accurate as the Intersector, which may be the most reliable bike-detection device for traffic signals available.
At $4,000 to $5,000, the device isn’t cheap. But Pleasanton, at least, was able to buy its Intersectors with sales tax cash from California’s Transportation Development Act. And San Jose recently scored $1.5 million from the Metropolitan Transportation Commission to track down and install the best bicycle detection devices. If cyclists are lucky, the lessons learned from California’s experiment in bike detection will be applied elsewhere.